Whom Should I Follow? Identifying Relevant Users in Crisis

Abstract

Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.

BibTeX Copy

@inproceedings{shamanth2013whom,
  title = {Whom Should I Follow? Identifying Relevant Users in Crisis},
  author = {Shamanth Kumar and Fred Morstatter and Reza Zafarani and Huan Liu},
  year = {2013},
  keywords = {conference},
  booktitle = {Proceedings of the 24th ACM Conference on Hypertext and Social Media (HT)},
  abstract = {Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.},
  url = {http://dl.acm.org/citation.cfm?id=2481507},
  pdf = {http://www.public.asu.edu/~huanliu/papers/ht2013.pdf},
}